CDN load balancing in the cloud

ABSTRACT

CND load balancing in the cloud. Server resources are allocated at an edge data center of a content delivery network to properties that are being serviced by edge data center. Based on near real-time data, properties are sorted by trending traffic at the edge data center. Server resources are allocated for at least one property of the sorted properties at the edge data center. The server resources are allocated based on rules developed from long-term trends. The resource allocation includes calculating server needs for the property in a partition at the edge data center, and allocating the server needs for the property to available servers in the partition.

BACKGROUND

Many Internet-based service providers deliver digital content to clientsaround the world. Digital content may include web objects (e.g., text,graphics, URLs, scripts), downloadable objects (e.g., media files,software, documents), web applications, streaming media (e.g. audio andvideo content), etc. Providing digital content to numerous clients thatare located in a broad variety geographical locations can presentchallenges to service providers. For example, a service provider may beunable to provide sufficient server resources and/or network bandwidthto serve all clients requesting digital content at a given time. Inaddition, clients that are geographically remote from a serviceprovider's servers may experience high levels of latency and/or lowtransfer rates as traffic between the service provider and the client isrouted through a large number of Internet servers and over greatgeographical distances.

Content Delivery Networks (CDNs) aim to ease the ability of serviceproviders to deliver digital content to large and/or geographicallydiverse groups of clients. CDNs position servers (or clusters ofservers) in various geographical locations, and use these servers tocache and deliver content from origin servers of service providers. Assuch, CDNs can improve the service providers' ability to deliver contentto clients both by increasing total available server resources andbandwidth used to deliver each service provider's content, and also bydelivering each service provider's content from servers that aregeographically nearer the clients that are being served.

CDNs often provide content delivery services for a large number ofservice providers. As such, CDNs allocate CDN resources among thevarious service providers. For example, if the CDN is experiencing asurge in traffic for a particular service provider in a particulargeographical region, the CDN may reactively allocate additional serverresources in the particular geographical region for use in deliveringthe particular service provider's content, while removing the additionalserver resources in the particular geographical region from one or moreother service providers.

BRIEF SUMMARY

At least some embodiments described herein leverage both live andhistorical data to proactively, as opposed to reactively, reconfigure aCDN to handle current and anticipated client loads. As such, based onlive and historical data, the embodiments described herein canproactively have server allocations in place for a service provider, andcontent of the service provider cached, before the client load for theservice provider spikes.

In some embodiments, server resources at an edge data center of a CDNare allocated to properties serviced by the edge data center. Based onnear real-time data, a computer system sorts properties by trendingtraffic at the edge data center. The computer system allocates serverresources for a property of the sorted properties at the edge datacenter based on rules developed from long-term trends. Allocationincludes the computer system calculating server needs for the propertyin a partition at the edge data center. Allocation also includes thecomputer system allocating the server needs for the property toavailable server(s) in the partition.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1 illustrates an example computer architecture that facilitatesboth proactively and reactively configuring a CDN to handle current andanticipated client loads for properties hosted by the CDN.

FIG. 2 illustrates an example scheme for dividing, partitioning, and/orallocating resources of an example CDN.

FIG. 3 illustrates a flow chart of an example method for allocatingserver resources at an edge data center of a CDN to properties servicedby the edge data center.

FIG. 4 illustrates a flow chart of an example method for a load balanceragent at an edge data center to offload traffic to another edge datacenter.

FIG. 5 illustrates a flow chart of an example method for determining oneor more attributes of a property using an incubation pool.

DETAILED DESCRIPTION

At least some embodiments described herein leverage both live andhistorical data to proactively, as opposed to reactively, reconfigure aCDN to handle current and anticipated client loads. As such, based onlive and historical data, the embodiments described herein canproactively have server allocations in place for a service provider, andcontent of the service provider cached, before the client load for theservice provider spikes.

More particularly, embodiments are directed to a CDN that services aplurality of properties and that includes a plurality of edge datacenters that are physically located at different geographic locations(and potentially at geographic locations around the world). Each edgedata center includes a plurality of servers that are used to cache andserve content of the properties. Embodiments are also directed to a CDNthat includes a load balancer service that tracks long-term traffictrends for the plurality of edge data centers, and that manages rulesfor allocating server resources (based on the long term traffic trends)to the properties. Embodiments are also directed to a CDN that includesa load balancer agent at each edge data center. Each load balancer agentis configured to make server allocation decisions based on the rulesthat are based on long-term traffic trends, as well as real-time (ornear real-time) data regarding present activity at the edge data center.As such, the load balancer agents make server allocation decisionsproactively based on long-term traffic trends and rules, and reactivelybased on real-time (or near-real time) data.

As used herein, a “property” is a customer web application that ishosted on an origin server. For example, a “property” may be an onlinevideo streaming website, an online audio streaming service, apatch/updates website, etc. As used herein, a “customer” is a person,entity, service provider, etc. that owns one or more properties. As usedherein, an “origin server” is a web server that is owned and/or operatedby a customer.

FIG. 1 illustrates an example computer architecture 100 that facilitatesboth proactively and reactively configuring a CDN to handle current andanticipated client loads for properties hosted by the CDN. Referring toFIG. 1, computer architecture 100 includes edge data centers 102, one ormore components 108 related to near real-time (NRT) data, and one ormore components 114 related to long-term trends and rules. Each of thedepicted components and computer systems are connected to one anotherover (or are part of) a network, such as, for example, a Local AreaNetwork (“LAN”), a Wide Area Network (“WAN”), the Internet, etc.Accordingly, each of the depicted components and computer systems cancreate message related data and exchange message related data (e.g.,Internet Protocol (“IP”) datagrams and other higher layer protocols thatutilize IP datagrams, such as, Transmission Control Protocol (“TCP”),Hypertext Transfer Protocol (“HTTP”), Simple Mail Transfer Protocol(“SMTP”), etc.) over the network.

Edge data centers 102 comprise a plurality of data centers that are eachlocated at different geographical locations. For example, FIG. 1 depictsedge data center 102 a and edge data center 102 b, though computerarchitecture 100 could include any number of edge data centers, asindicated by the vertical ellipses. As used herein, a “pop” (point ofpresence) also refers to an edge data center, whether the edge datacenter is dedicated as part of a CDN, or is collocated with otherservices.

As depicted, each edge data center 102 includes a plurality of servers(e.g., servers 106 a and 106 b) that are configured to cache content ofproperties that are associated with the edge data center and to deliverthat content to clients (e.g., clients of the cached properties, thatare geographically near the edge data center). Each edge data center 102also includes a load balancer agent (e.g., load balancer agents 104 aand 104 b). The load balancer agents (104 a, 104 b) are configured tomake both proactive and reactive decisions about how server resources(106 a, 106 b) at the corresponding edge data centers are allocated todifferent properties. In doing so, the load balancer agents (104 a, 104b) use rules (from a rules data store 124) that are based on long termstrends data, as well as NRT data (from a NRT data store 112).

As used herein, “NRT data” means data that is collected and aggregatedin near real-time, such as within a few minutes or even within seconds.In the depicted embodiment, the component(s) 108 related to NRT datainclude a NRT aggregator 110 and NRT data store 112. NRT aggregator 110parses logs generated at edge data centers 102 to generate NRT dataabout current activity at each edge data center. NRT aggregator 110stores the generated NRT data in NRT data store 112. NRT data store 112makes the NRT data available to load balancer agents (104 a, 104 b) ateach edge data center (as indicated by the arrows between NRT data store112 and the load balancer agents). Correspondingly, the load balanceragents are enabled to retrieve the NRT data from NRT data store 112 andto use the NRT data as part of their resource allocation decisions.Using NRT data, the load balancer agents are able to react to currenttraffic at the edge data centers 102, such as to deal with unexpectedsurges in traffic at one or more properties. While the component(s) 108related to NRT data are depicted as being separate from edge datacenters 102, all or part of these components may, in some embodiments,be implemented at the edge data centers 102.

In the depicted embodiment, the component(s) 114 related to long-termtrends and rules include logs data store 120, trends aggregator 118, andlong-term trends data store 116. Logs data store 120 is configured tostore logs generated by edge data centers 102. Trends aggregator 118 isconfigured to parse these logs to ascertain long-term traffic patternsof the properties that are serviced by the edge data centers. Tendsaggregator 118 stores long-term traffic patterns in long-term trendsdata store 116.

Long-term trends may identify changes in client demand for a propertyover days, weeks, months, or even years. For example, for avideo-streaming property, long-term trends may indicate an increase inclient demand on a particular day of the week (e.g., corresponding tothe release of new content), in the evenings, and on the weekends. Inanother example, for a patch/updates property, long-term trends mayindicate an increase in client demand on a particular day of the month(e.g., when new patch/update content is released), and at a particulartime of day when many clients are configured to install patches/updates.

In the depicted embodiment, the component(s) 114 related to long-termtrends and rules also include load balancer service 122 and rules datastore 124. Load balancer service 122 is configured to analyze thetraffic pattern data in long-term trends data store 116, tocreate/modify rules related to the assignment of servers at edge datacenters 102 to various properties, and to store the rules in rules datastore 124. Rules data store 124 makes the rules available to loadbalancer agents (104 a, 104 b) at each edge data center (as depicted bythe arrows between rules data store 124 and the load balancer agents).Correspondingly, the load balancer agents are enabled to retrieve therules from rules data store 124 and to use the rules as part of theirresource allocation decisions.

As indicated by the double arrow between long-term trends data store 116and load balancer service 122, load balancer service 122 may receivelong-term trend data from long-term trends data store 116, and alsoprovide feedback to long-term trends data store 116. For example, loadbalancer service 122 may refine/optimize the gathering of long-termtrend data. In addition, as indicated by the double arrow between rulesdata store 124 and load balancer service 122, load balancer service 122may both read rules from rules data store 124 and also provide rulesinput and adjustments to rules data store 124.

In some embodiments, a CDN assigns properties to pools that span edgedata centers (pops). The set of servers at a particular pop that belongto a particular pool are a partition of that pool. These servers servicethe properties for the pool at the pop. FIG. 2, for example, illustratesan example scheme for dividing, partitioning, and/or allocatingresources of an example CDN. The example CDN includes three pops (oredge data centers). These include “pop A” with 50 servers, “pop B” with80 servers, and “pop C” with 100 servers. As indicated by the horizontalellipses, the example CDN could include any number of pops.

As depicted, the example CDN divides the example CDN resources into aplurality of pools, including ‘pool 1’, ‘pool 2’, and ‘pool 3’. Asindicated by the vertical ellipses, the example CDN could include anynumber of pools. Each pool comprises a partition of one or more serversat one or more of the pops. For example, pool 1 includes partition 1A(10 servers) at pop A, partition 1B (25 servers) at pop B, and partition1C (20 servers) at pop C. As depicted, pool 2 includes partitions (2A,2B, and 2C) at pops A, B, and C, and pool 3 includes partitions (3A and3B) at pops A and B.

Properties can be assigned to the pools. For example, five propertiesmay be assigned to pool 1, 10 properties may be assigned to pool 2, andfour properties may be assigned to pool 3. Correspondingly, servers inpartitions of each pool are reserved to service the properties that areassigned the pool. For example, at pop A the servers of partition 1A arereserved to service the five properties assigned to pool 1, at pop B the25 servers of partition 1B are reserved to service the five propertiesassigned to pool 1, and at pop B the 20 servers of partition 1C arereserved to service the five properties assigned to pool 1. In someembodiments, a property may be assigned to multiple pools, and thosepools may be assigned to the same pop. Thus, a property may be assignedto multiple partitions (corresponding to different pools) at a singlepop.

Within the context of FIGS. 1 and 2, there are a plurality ofmethodologies that can be employed as part of managing serverallocations for servicing properties within a CDN.

1. Load-Balancing Algorithm

Embodiments include a load-balancing algorithm that is performed by eachload balancer agent at edge data centers 102. Using the load-balancingalgorithm, the each load balancer agent leverages both NRT data from NRTdata store 112 and rules from rules data store 124 to make local serverallocation decisions.

FIG. 3 illustrates a flow chart of an example method 300 for allocatingserver resources at an edge data center of a CDN to properties servicedby the edge data center. Method 300 will be described with respect tothe components and data of computer architecture 100.

Method 300 comprises an act of sorting properties according to trendingtraffic (act 302). Act 302 can include an act of sorting, based on nearreal-time data, a plurality of properties by trending traffic at an edgedata center. For example, load balancer agent 104 a can compare NRT datafrom NRT data store 112 with rules from rules data store 124 todetermine the type of traffic pattern one or more properties areundergoing at edge data center 102. Load balancer agent 104 a can thensort the properties that are experiencing traffic surges (e.g., indescending order of priority) and place them in a sorted property list.The remainder of method 300 can then make resource allocationassignments according to the sorted property list so that surgingproperties are allocated server resources 106 a before non-surgingproperties.

Method 300 also comprises an act of selecting the next property in thesorted property list (act 304) and an act of selecting the nextpartition for the selected property (act 306). For example, loadbalancer agent 104 a can select the property having the greatesttrending traffic in the sorted property list, and then select a firstpartition to which that property is assigned.

Method 300 also comprises an act of calculating server needs for theselected property in the selected partition (act 308). Act 308 caninclude an act of allocating server resources for at least one propertyof the sorted plurality of properties at the edge data center based onone or more rules developed from long-term trends, including calculatingserver needs for the at least one property in a partition at the edgedata center. For example, act 308 can include load balancer agent 104 achanging the load type of the property based on trending traffic anddetermining the server needs for the property based on the new loadtype.

In some embodiments, properties can be assigned a general load typewithin the rules, based on factors such as past traffic patterns for theproperty (e.g., as described later in connection with incubation),manual assignments, or specification from a customer. For example, eachproperty may be categorized in each pop according to a “t-shirt size”(e.g., S, M, L, XL) based on its typical client load at that pop. Thus,in one embodiment, act 308 can include a load balancer agent using theNRT data to determine that current trending traffic will cause theproperty to reach a point that will result in a load type increase(e.g., from L to XL) or decrease at the pop. Based on the determinationthat the property needs a load type increase/decrease, the load balanceragent can assign the new load type to the property at the pop. Then, theload balancer agent can compute new server needs for the property basedon the new load type.

Computing the new server needs can include retrieving the general serverneeds for a property of the new load type from the rules. For example,the rules may specify the general server needs of a property of loadtype XL are as shown in Table 1:

TABLE 1 Property Load Type Minimum Server Needs Multiplier XL 20 10

Table 1 specifies minimum server needs and a multiplier. The minimumserver needs is the count of totally dedicated servers capable offulfilling 100% of the needs of a property of this type at the pop. Themultiplier defines how much elastic margin should be allowed for aproperty of this type.

Computing the new server needs can also include calculating the pop loadfor the property. For example, based on its pool assignment, a propertymay be allocated across pops as shown in Table 2:

TABLE 2 Pop Allocation Property ID Pop ID Load Property 1 Pop A XLProperty 1 Pop B XL Property 1 Pop C XL Property 1 Pop D L Property 1Pop E L Property 1 Pop F SIn addition, each load type can correspond to a coefficient weight. Forexample coefficient weights may be as specified in Table 3:

TABLE 3 Load Type Weight XL 5 L 3 S 1Based on tables 1-3, the load balancer agent can calculate the serverneeds count for each load type for the property, as shown in Table 4:

TABLE 4 Load Type Count XL 3 * 5 (number of XL loads types, times 15weight for XL load type) L 2 * 3 (number of L load types, times weight 6for an L load type) S 1 * 1 (number of S load types, times weight 1 fora S load type) Sum Total 22The load balancer agent can then compute the allocation percentage forthe selected property in the selected partition using the followingequation:allocation percentage=((number of a load type at a pop)*(weight of theload type from Table 3))/(sum total from Table 4)For example, for pop A, the allocation percentage would be computed as:(1*5)/22=0.227 or ˜22%. Table 5 shows the allocation percentage forproperty 1 in each pop:

TABLE 5 Allocation Property ID Pop ID Percentage Property 1 Pop A (1 *5)/22 = ~22% Property 1 Pop B (1 * 5)/22 = ~22% Property 1 Pop C (1 *5)/22 = ~22% Property 1 Pop D (1 * 3)/22 = ~14% Property 1 Pop E (1 *3)/22 = ~14% Property 1 Pop F (1 * 1)/22 = ~5%Finally, the number of servers needed for property 1 in each pop can becomputed with the following equation:number of servers needed=(minimum server needs from Table 1)*(multiplierfrom Table 1)*(allocation percentage)For example, property 1 in pop A would need 20*10*22%=44 servers. Thus,the new server needs for the property 1 in the partition at pop A wouldbe 44 servers.

In some cases, a property may be assigned to two or more overlappingpools. In such cases, the load balancer agent can calculate the poolpartition share of the load in the pop. For example, property 1 may beassigned to both a ‘North America Pool’ and an ‘International Pool’,with each pool being assigned to at least one common pop. For example,Table 6 depicts an example pool allocation scheme in which the pools areassigned to at least one common pop:

TABLE 6 Pool ID Pop ID Server Count North America Pool Pop A 80 NorthAmerica Pool Pop B 50 North America Pool Pop C 20 International Pool PopA 100 International Pool Pop G 50 International Pool Pop H 35In this pool allocation scheme, property 1 is assigned to one partition(80 servers) in pop A as part of the North America Pool, and anotherpartition (100 servers) in pop A as part of the International Pool, fora total of 180 servers at pop A. Calculating the partition share in popA can be accomplished using one of at least two strategies: naïvepartition allocation or proportional partition allocation.

Using naïve partition allocation, the load balancer agent can assign theservers needed by the property in the pop to each partition evenly. Forexample, since property 1 needs 44 servers in pop A, the load balanceragent can assign half of the server needs (22 servers) to the partitioncorresponding to the North America Pool and half of the server needs (22servers) to the partition corresponding to the International Pool.

Using proportional partition allocation, by contrast, the load balanceragent can assign the servers needed by the property in the pop to eachpartition proportionally. For example, the partition at pop Acorresponding to the North America Pool has ˜44% (80 servers/180servers) of the servers potentially available to property 1 at pop A,and the partition at pop A corresponding to the International pool has˜55% (100 servers/180 servers) of the servers potentially available toproperty 1 at pop A. Thus, since property 1 needs 44 servers in pop A,the load balancer agent can proportionally assign 20 of the server needs(44 servers*44%) to the partition corresponding to the North AmericaPool and 24 of the server needs (44 servers*55%) to the partitioncorresponding to the International Pool, for a total of 44 servers.

Returning to FIG. 3, method 300 also comprises an act of allocating theproperty needs to the available servers in the selected partition (act310). Act 308 can include an act of allocating server resources for atleast one property of the sorted plurality of properties at the edgedata center based on one or more rules developed from long-term trends,including allocating the server needs for the at least one property toone or more available servers in the partition. For example, act 310 caninclude load balancer agent 104 a allocating the load share to serversin a partition using one or more different criteria. For example, duringallocation, load balancer agent 104 a can perform one or more of: (i)aggregating server resources, (ii) accounting for stickiness of data, or(iii) accounting for compatibility of properties.

For example, act 308 can include sorting servers in a partition that arecandidates to be assigned to a property according to a weighted averageof a resources aggregates index, a stickiness of cached data index, anda compatibility index. For example, a load balancer agent can compute aweighted average for each candidate server in a partition that aproperty may be assigned to, then sort those servers according to theweighted average. The sorted list of servers provides, in order, thebest suitable servers for assignment to the property.

The resources aggregates index provides an indication of an estimate ofavailable resources remaining at a server, and can be computed in anyappropriate manner for estimating remaining resources at a server. Forexample, a load balancer agent can track which properties are allocatedto which servers in a partition, and estimate available resourcesremaining each server given the average load of these properties.

The stickiness of cached data index can indicate how long data has beencached at a server. For example, a load balancer agent can track howlong cached data at the servers has been alive. The longer data has beencached, the more valuable the stickiness, since this data would appearto be more valuable.

The compatibility index can provide a score of the compatibility (orincompatibility) of different properties at a server. For example, aload balancer agent can score a server based on the compatibility ofproperties that are assigned to the server, while factoring in a penaltyfor the presence of incompatible properties. The load balancer agent canuse the compatibility index to minimize incompatible propertyassignments. For example, since a property that primarily uses networkI/O resources may be compatible with a property that primarily uses diskI/O resources, the load balancer agent may assign these properties tothe same server, while avoiding assigning other properties that alsoheavily use network I/O and disk I/O to that server.

Computing a compatibility index can include: (i) obtaining, from therules, a predefined penalty coefficient for incompatible property typesand sizes; (ii) using a compatibility matrix from the rules to obtain alist of all incompatible properties that are hosted at a server, (iii)identifying the load types (e.g., S, M, L, XL) of properties on theserver and the frequencies of their occurrence on the server; and (iv)for each incompatible property type, raise their penalty coefficient tothe power of the frequency. Computing the compatibility index can besummarized with the following equation:

${{compatibility}\mspace{14mu}{index}} = {\left( {\prod\limits_{i = 1}^{n}{c_{i}}^{f_{i}}} \right)*100}$where:

n=incompatible properties count of a certain load type,

c=compatibility penalty coefficient, and

f=frequency of occurrence of the incompatible property of the certainload type.

For example, Table 7 represents an example compatibility matrix, whichwould be defined in the rules, that specifies which property types arecompatible with each other:

TABLE 7 Type 1 Type 2 T1 T2 T4 T1 T4 T2 T4 T3For example, property type T1 may be a property that primarily uses diskI/O, while property type T2 may be a property that primarily usesnetwork I/O. In addition, Table 8 represents an example penaltycoefficient, defined in the rules, that specifies the penalty forincompatible properties of certain sizes:

TABLE 8 Compatibility Penalty Load Type Coefficient XL 0.6 M 0.7 S 0.90If, given the above compatibility index, a server were to be assignedthree incompatible XL-sized properties, two incompatible M-sizedproperties, and one incompatible S-sized property, the compatibilitycoefficient index for the server would be computed using the aboveformula as: (0.6)³*(0.7)²*(0.9)*100=9.52%. By making server assignmentsin a manner that maximizes the compatibility index score, the loadbalancer agent can maximize the assignment of compatible properties tothe server and make more efficient use of the server's resources.

As discussed previously, a load balancer agent can compute a weightedaverage for each candidate server in a partition that a property may beassigned to, and then sort those servers according to the weightedaverage. For example, if a property needs to be assigned to a partitionof five servers, computation of the weighted average for one server maybe performed as specified in Table 9:

TABLE 9 Criteria Criteria Weight Calculated Value Description Resources3 80% The higher the value, the Aggregates more resources are Indexavailable Stickiness of 1 30% The higher the value, the Cached Data morethe data is valuable Index Compatibility 2 75% The higher the score, theIndex more the property is compatible with the population of otherproperties on the server. Weighted 70% (80 * 3) + (1 * 75) + AverageValue (75 * 2) = 420/6 = 70%A similar weighted average can also be computed for the other fourservers in the partition. The five servers can then be sorted accordingto their weighted average value. Table 10 shows an example sorting:

TABLE 10 Server ID Score 1 70% 2 50% 3 50% 4 42% 5 38%The property can then be assigned to the servers according to the sortedorder, with servers having a higher score being more suitable for havingthe property assigned to them.

A property can be assigned to a server by updating a mapping ofproperties to servers. After making a property assignment, the loadbalancer agent can adjust the aggregate resource index for the assignedserver(s) to account for the resources that will be used by the newlyassigned property.

Method 300 may also comprise an act of handling server deficit (act312). In some instances, a pop may not have enough server resources in apartition to completely assign properties to available servers. Whenthis occurs, act 312 can include the load balancer agent at the poprequesting that some of the load be shared by servers at other pops.This is described further in connection with inter-agent load balancing.If no other pops can handle the requested load, the property may beassigned to the same server at the pop more than once. Act 312 may alsoinclude raising one or more alerts, such as to a CDN administrator.

Method 300 also comprises an act of determining whether there are morepartitions for the selected property (act 314) and an act of determiningwhether there are more properties in the sorted property list (act 316).When there are more partitions and/or properties, method 300 can loopback to acts 304 and/or 306, ensuring that all properties and partitionsare considered and any appropriate server assignments are made.

2. Inter-Agent Load Balancing with Priority

In some embodiments, a load balancer agent at an edge data center maydetermine (based on the rules) that load conditions at the edge datacenter have reached certain parameters that indicate an overload. Whenthis happens, the load balancer agent may contact other load balanceragent(s) at one or more other edge data centers to attempt to offsetsome of the traffic at the edge data center to one or more other edgedata centers. For example, FIG. 1 includes a double-arrow between edgedata center 102 a and edge data center 102 b, indicating that loadbalancer agent 104 a and load balancer agent 104 b can communicate withone another (and with other load balancer agents at other edge datacenters).

FIG. 4 illustrates a flow chart of an example method 400 for a loadbalancer agent at an edge data center to offload traffic to another edgedata center. Method 400 will be described with respect to the componentsand data of computer architecture 100.

Method 400 comprises an act of determining that traffic should beoffloaded (act 402). Act 402 can include an act determining that trafficat an edge data center should be offloaded to one or more other edgedata centers. For example, load balancer agent 104 a can consultbusiness rules from rules data store 124 to determine whether trafficshould be offloaded. In some embodiments, business rules can take theform of: if <condition> then <action>, using any appropriate structuredlanguage (e.g., XML, C#, Java). In some embodiments, example rules mayinclude:

-   -   If an agent finds a depletion of resources in the pop up to 70%,        then try to offload 25% of the traffic to other pops.    -   If an agent detects degradation in the health of its pop between        30% and 40%, then try to offload 50% of the traffic to other        pops. Health may be measured according to a health index in the        NRT data that scores pop health according to factors like how        many servers are in/out of rotation, how many servers are        loaded, etc.

Method 400 also comprises of an act of sending offload request(s) toother edge data center(s) (act 404). Act 404 can include an actdetermining a priority level for requesting the offloading of traffic tothe other edge data centers. For example, load balancer agent 104 a candetermine the urgency of offloading traffic to other edge data centers,and determine a priority level accordingly. Other load balancer agentscan use the priority when determining whether or not to lend resourcesto load balancer agent 104 a. In some embodiments, priority levels couldinclude the examples specified in Table 11:

TABLE 11 Priority Urgency Example 0 Critical Pop bandwidth is severelyreduced 1 High Several traffic surges have caused some heavily loadedproperties to jump load levels 2 Moderate There are some traffic surgesand the load is on the heavy side for some properties 3 Low There issome heavy load requiring extra resources

Act 404 can also include an act of sending an offload request to each ofthe other edge data centers, each offload request indicating thedetermined priority level. For example, load balancer agent 104 a cansend an offload request to load balancer agent 104 b at edge data center102 b. Load balancer agent 104 a may also send offload requests to oneor more other load balancer agents at other edge data centers that arenot depicted. When a load balancer agent sends offload requests to aplurality of other load balancer agents, the load balancer agent may doso in a timed, asynchronous manner.

Method 400 also comprises of an act of receiving responses to theoffload request(s) (act 406). Act 406 can include an act of receivingone or more replies from one or more of the other edge data centers,including one or more replies indicating that resources are availablefor use by the load balancer agent. For example, load balancer agent 104a may receive a reply from one or more of the edge data centers to whichit sent the offload request(s). The replies may indicate that the otheredge data centers have resources are available for use by the loadbalancer agent to offload traffic. In some embodiments, the repliesinclude a list of servers at the other edge data center that areavailable for use. In some embodiments, a reply indicates that theresources are guaranteed to be valid and reserved for use by loadbalancer agent 104 a for a predetermined or negotiated amount of time.Act 406 may also include receiving one or more replies indicating thatresources are not available for use by the load balancer agent (i.e.,assistance is not possible).

Method 400 also comprises of an act of offloading traffic to at leastone edge data (act 408). Act 408 can include an act of sorting the oneor more replies to identify at least one edge data center for offloadingtraffic. For example, if load balancer agent 104 a receives affirmativereplies from more than one edge data center, load balancer agent 104 acan sort these replies to determine to which edge data center(s) trafficshould be offloaded. In some embodiments, sorting the replies includescalculating a weighted average of factors for each data center. Thefactors can include, for example, distance (physical or network) of theedge data center, the cost (e.g., monetary cost for bandwidth) foroffloading traffic to the edge data center, and/or the resources thathave been made available at the edge data center. For example, eventhough one edge data center may have a higher monetary cost for use thanother available edge data centers, it may be desirable to use that edgedata center because it can provide a larger number of resources andother available edge data centers and/or because is it closer than otheravailable edge data centers.

Act 408 can include an act of offloading traffic to the at least oneidentified edge data center. For example, once a desired edge datacenter is selected, load balancer agent 104 a can send a list of serversthat are desired to be used to the identified edge data center, alongwith a resource manifest. Load balancer agent 104 a may offload sometraffic to one edge data center, and offload the remaining traffic toone or more other edge data centers.

If an edge data center has made resources available to load balanceragent 104 a and load balancer agent 104 a will not be making use ofthose resources (e.g., because it chose to use resources at another edgedata center), load balancer agent 104 a can inform the edge data centerthat those resources can be freed. For example, load balancer agent 104a may inform an edge data center that some, or all, of the servers theedge data center made available/reserved can be freed.

Accordingly, method 400 enables an edge data center to leverageresources at other edge data centers when it determines that it is in oris approaching an overload situation.

3. Feedback Loop Between the Central Service and the Rules Database

As discussed previously, load balancer service 122 is, in someembodiments, in two-way communication with long-term trends data store116 and rules data store 124. As such, load balancer service 122 can beconfigured to engage in a feedback loop to self-assess and toself-improve the rules based on the long-term trends. Using long-termdata from the long-term trends data store 116 as a primary input, theload balancer service 122 can determine optimized settings forconfiguration parameters and business rules that are in rules data store124.

In some embodiments, the feedback loop occurs explicitly. For example,when a change is made to a configuration value (e.g., a compatibilitypenalty, a sort criteria weight), load balancer service 122 can make asnapshot of performance data for the CDN, for a pop, for a property,etc. Then, load balancer service 122 can subsequently analyze thesnapshot to ascertain the effect of the change to the configurationvalue on performance of the relevant component. If there is performancedegradation in the CDN due the change, an alert can be generated and/orthe configuration value can be automatically adjusted (e.g., reverted).

In additional or alternative embodiments, the feedback loop occursimplicitly. Since log data may be continuously aggregated (e.g., bytrends aggregator 118) and analyzed (e.g., by load balancer service122), and since load balancer service 122 is configured to recognizedand adapt to changes in the log data (e.g., daily), changes to the loadof a property and/or type of resource consumption by a property shouldbe accounted for eventually.

The feedback loop can work on both live and on simulated logs, and canoperate on both live and simulated configuration parameters\rules. Table12 specifies how implicit and explicit elements of the feedback loop maywork on each type.

TABLE 12 Mode Implicit Explicit Live Long-term traffic logs are Changesmade to live operating mined and parameters are environment will triggera adjusted automatically monitoring job that closely within limitswatches performance metrics and will alert in case of performancedegradation Simulation Induced logs can help test Help model the impactof the level of adjustments configuration and rules changes generatedover time4. Incubation

Some embodiments include the use of one or more incubation pools.Generally, when a property is first added to a CDN, the CDN can assignthe property to an incubation pool. When in an incubation pool, theproperty is analyzed to ascertain one or more attributes of theproperty, such as t-shirt size (e.g., S, M, L, XL) and traffic patterns(e.g., peak traffic periods, low traffic periods, etc.). Once theproperty has been analyzed within the context of the incubation pool,the property can be assigned to a more general pool.

FIG. 5 illustrates a flow chart of an example method 500 for determiningone or more attributes of a property using an incubation pool. Method500 will be described with respect to the components and data ofcomputer architecture 100.

Method 500 comprises an act of determining an incubation period (act502). Act 502 can include an act of determining an incubation periodbased on an estimate of load size and traffic type for a property andbased on one or more rules that define a minimum and a maximumincubation time for properties that are added to an incubation pool. Forexample, load balancer service 122 or another administrative systemwithin a CDN can determine an incubation period for a property that isto be added to the CDN. Determining the incubation period can be basedon information that is provided about the property (e.g., from thecustomer), and based on general business rules in rules data store 124.

In some embodiments, determining the incubation period is based on acustomer truth index (CTI), which can affect the amount of time that aproperty spends in incubation. If a customer has a high CTI, meaning theCDN has a high level of trust in how a customer characterizesproperties, then the CDN may leave that customer's newly addedproperties in incubation for a shorter period of time. By contrast, if acustomer has a low CTI, meaning the CDN has a low level of trust in howa customer characterizes properties, then the CDN may leave thatcustomer's newly added properties in incubation for a longer period oftime.

For example, when a customer first on-boards a property to the CDN, theCDN may ask the customer one or more questions about the property. Thesequestions can be designed to help the CDN compute a generalapproximation of attributes of the property (e.g., load size and type oftraffic). If this is the first property that is being on-boarded by thecustomer, the CDN may apply a relatively low level of trust to theanswers that are supplied by the customer, and leave the property inincubation for a longer period of time.

After the property goes into incubation, the CDN can compute theattributes that were previously estimated based on observation of realuse of the property. If the computed attributes are similar to thosethat were approximated based on customer input, then the customer may beassigned a high CTI. If the computed attributes are dissimilar fromthose that were approximated based on customer input, then the customermay be assigned a low CTI. The CDN may refine the CTI of a customer overtime, such as by re-computing the attributes after the property has beenreleased to a production pool and updating the CTI accordingly.

In some embodiments, the CTI is a composite index that is expressed as apercentage. The CTI may be based on the customer's track record oftelling the “truth” about property characteristics. The CTI may also bebased on the actual performance of the property. The customer's trackrecord and the actual performance of the property may be weighteddifferently (e.g., a weight of one for customer's truth and a weight oftwo for actual property performance). For example, if a customer has ahistoric CTI of 80%, and during the incubation of a new property theactual performance is 90% of the customer's estimates, the new CTI maybe computed as (80+(90*2))/3=˜86%.

As indicated, determining the incubation period is based on businessrules in rules data store 124. For example, the rules may specify aminimum incubation period and a maximum incubation period. The finalincubation period may be between the minimum and maximum, based on theCTI. For example, if a customer has a CTI of 90% or greater, then newproperties for the customer may be left in incubation for only theminimum period. If the customer has a CTI of less than 90%, then newproperties for the customer may be left in incubation for a periodgreater than the minimum, and approaching the maximum as the CTI scoregets lower.

Method 500 also comprises an act of adding a property to an incubationpool (act 504). Act 504 can include an act of adding the property to theincubation pool, including allocating one or more server resources ofthe incubation pool to the property. For example, load balancer service122 or another administrative system within the CDN can assign theproperty to a special pool that is set apart as an incubation pool. Assuch, the property can be assigned to corresponding partitions at edgedata centers/pops.

Act 504 can also include an act of analyzing load and traffic patternsfor the property for the determined incubation period. For example,during its time in the incubation pool, load balancer service 122 cananalyze the client load patterns, the type of content that is served bythe property, the types of resources that are utilized at servers ofedge data centers when serving clients, etc. Act 504 can also include anact of determining one or more of a load size or a traffic type for theproperty based on adding the property to the incubation pool. Forexample, using the data collected during incubation (e.g., client loadpatterns, the type of content that is served by the property, the typeof resources that are utilized at servers of edge data centers), the CDNcan compute the size and traffic type of the property, and store thisinformation in rules data store 124.

Method 500 may also comprise an act of handling incubation spillover(act 506). For example, the rules in rules data store 124 may specifyconditions under which a newly incubated property can spill over itsallocated resources and use additional resources. In one example, therules may specify that a property for a VIP customer may beautomatically allowed to spill over, and that spillover is disabled bydefault for non-VIP properties. In another example, the rules mayspecify that spillover can be enabled for a property if the propertyexhibits a constant growth that exceeds certain thresholds. The rulesmay also specify conditions for ceasing a spillover.

Act 506 can include an act of determining that the property exceeds theone or more allocated server resources, and allocating one or moreadditional resources to the property. For example, load balancer service122 or another administrative system within the CDN may determine that aproperty is growing larger than its allocated resources, and that is itpermitted to spill over. In response, the CDN may allocate the propertygreater resources within the incubation pool, spill over to resourceswithin another incubation pool, and/or spill over to resources within anon-incubation pool (e.g., for a VIP customer).

Method 500 may also comprise an act of executing post-incubation steps(act 508). For example, after the incubation period has ended, it may bedetermined that insufficient information has been gathered about theproperty. As such, the incubation period for the property may beextended. In another example, the CTI index for the property's customermay be updated, as described above. In yet another example, the customermay be billed for time the property spent in the incubation phase,and/or billing rules may be updated based on information gathered duringincubation.

Method 500 may also comprise stressing the property. For example, whilethe property is in incubation, the CDN may run a stress test load on theproperty. Doing so can help to determine the property's behaviorpatterns and the property's impact on the CDN during extreme loadscenarios. In some embodiments, a customer may be given favorablebilling treatment if they opt to be subject to a stress test. Forexample, during on-boarding the customer may be offered an option toexecute a stress test against the property. The customer may be able tospecify stress test parameters, such as stress test duration, thetype(s) of synthetic test data to use as part of the stress test, etc.The types and boundaries of stress test parameters can be defined in therules. The CDN can use un-utilized or under-utilized resources withinthe CDN, special-purpose resources within the CDN, and/or resources thatare separate from the CDN to generate the stress load.

Accordingly, prior to unleashing a property on general CDN resources,incubation can help to acquire data about a property and to define rulessurrounding the property. Doing so can help to refine the CDN'srelationships with customers, and to protect the integrity of CDNresources.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above,or the order of the acts described above. Rather, the described featuresand acts are disclosed as example forms of implementing the claims.

Embodiments of the present invention may comprise or utilize aspecial-purpose or general-purpose computer system that includescomputer hardware, such as, for example, one or more processors andsystem memory, as discussed in greater detail below. Embodiments withinthe scope of the present invention also include physical and othercomputer-readable media for carrying or storing computer-executableinstructions and/or data structures. Such computer-readable media can beany available media that can be accessed by a general-purpose orspecial-purpose computer system. Computer-readable media that storecomputer-executable instructions and/or data structures are computerstorage media. Computer-readable media that carry computer-executableinstructions and/or data structures are transmission media. Thus, by wayof example, and not limitation, embodiments of the invention cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media and transmission media.

Computer storage media are physical storage media that storecomputer-executable instructions and/or data structures. Physicalstorage media includes recordable-type storage devices, such as RAM,ROM, EEPROM, solid state drives (“SSDs”), flash memory, phase-changememory (“PCM”), optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other physical storage medium which canbe used to store program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system.

Transmission media can include a network and/or data links which can beused to carry program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system. A “network” isdefined as one or more data links that enable the transport ofelectronic data between computer systems and/or modules and/or otherelectronic devices. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computersystem, the computer system may view the connection as transmissionmedia. Combinations of the above should also be included within thescope of computer-readable media.

Further, upon reaching various computer system components, program codein the form of computer-executable instructions or data structures canbe transferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a “NIC”), and theneventually transferred to computer system RAM and/or to less volatilecomputer storage media at a computer system. Thus, it should beunderstood that computer storage media can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at one or more processors, cause ageneral-purpose computer system, special-purpose computer system, orspecial-purpose processing device to perform a certain function or groupof functions. Computer-executable instructions may be, for example,binaries, intermediate format instructions such as assembly language, oreven source code.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The inventionmay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. As such, ina distributed system environment, a computer system may include aplurality of constituent computer systems. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Those skilled in the art will also appreciate that the invention may bepracticed in a cloud computing environment. Cloud computing environmentsmay be distributed, although this is not required. When distributed,cloud computing environments may be distributed internationally withinan organization and/or have components possessed across multipleorganizations. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services). The definition of “cloudcomputing” is not limited to any of the other numerous advantages thatcan be obtained from such a model when properly deployed.

A cloud computing model can be composed of various characteristics, suchas on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, and so forth. A cloud computing model mayalso come in the form of various service models such as, for example,Software as a Service (“SaaS”), Platform as a Service (“PaaS”), andInfrastructure as a Service (“IaaS”). The cloud computing model may alsobe deployed using different deployment models such as private cloud,community cloud, public cloud, hybrid cloud, and so forth.

Some embodiments, such as a cloud computing environment, may comprise asystem includes one or more hosts that are each capable of running oneor more virtual machines. During operation, virtual machines emulate anoperational computing system, supporting an operating system and perhapsone or more other applications as well. In some embodiments, each hostincludes a hypervisor that emulates virtual resources for the virtualmachines using physical resources that are abstracted from view of thevirtual machines. The hypervisor also provides proper isolation betweenthe virtual machines. Thus, from the perspective of any given virtualmachine, the hypervisor provides the illusion that the virtual machineis interfacing with a physical resource, even though the virtual machineonly interfaces with the appearance (e.g., a virtual resource) of aphysical resource. Examples of physical resources including processingcapacity, memory, disk space, network bandwidth, media drives, and soforth.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed:
 1. A method, implemented at a computer system thatincludes one or more processors and system memory, for improvingperformance of a content delivery network (CDN) by reallocating serverresources at an edge data center of the CDN to web applicationproperties serviced by the CDN, the method comprising: proactivelyallocating server resources of the edge data center among a plurality ofweb application properties, based on one or more rules that aredeveloped from first data collected about the plurality of webapplication properties over a first time frame, and that identifyhistoric network demand for each of the plurality of web applicationproperties, including: using the one or more rules to determine a loadtype category for each web application property; calculating at leastone compatibility index for each of a plurality of servers at the edgedata center, including applying a penalty coefficient to at least oneserver hosting a property type incompatible with at least one of theplurality of web application properties; creating at least one sortedorder of the plurality of servers based on the at least onecompatibility index calculated for each of the plurality of servers;using the load type category of each web application property and the atleast one sorted order of the plurality of servers to allocate acorresponding amount of server resources at the edge data center to eachweb application property, the server resources allocated to each webapplication property used to cache data of the web application propertyand to serve the cached data to network clients, wherein the one or morerules cause web application properties having higher historic networkdemand to be allocated a greater amount of server resources at the edgedata center than web application properties having lower historicnetwork demand; and sending data of each web application property over anetwork to the edge data center, for caching the data of each webapplication property at its corresponding allocated server resources andserving the cached data to network clients from the correspondingallocated server resources; and reactively re-allocating the serverresources of the edge data center among the plurality of web applicationproperties based on second data that is collected over a second timeframe that is shorter than the first time frame, to identify currentactivity at the edge data center, and based on identifying currentlytrending traffic associated with one or more of the plurality of webapplication properties at the edge data center from the second data,including: determining from the second data that client network demandsfor at least one of the plurality of web application properties iscurrently increasing, and that trending traffic has caused the at leastone web application property to experience a load type categoryincrease; based on the at least one web application property havingexperienced a load type category increase, calculating additional serverresources at the edge data center needed to meet the current clientnetwork demands for the at least one web application property based on anew load type category for the at least one web application property;and allocating the additional server resources at the edge data centerto the at least one property, for caching the data of the at least oneweb application property at the additional server resources and servingthe cached data to network clients from the additional server resources.2. The method as recited in claim 1, wherein allocating the additionalserver resources for the at least one web application property comprisesallocating server resources from a plurality of partitions at the sameedge data center.
 3. The method as recited in claim 1, wherein creatingthe at least one sorted order of the plurality of servers is also basedon a weighted average of one or more resources aggregates indexes the atleast one compatibility index calculated for each of the plurality ofservers.
 4. The method as recited in claim 1, further comprising:handling server deficit.
 5. The method as recited in claim 4, whereinhandling server deficit comprises requesting resources from one or moreother edge data centers.
 6. A computer program product comprising one ormore hardware storage devices storing computer-executable instructionsthat are executable by one or more processors of a computer system tocause the computer system to reallocate server resources at an edge datacenter of a content delivery network (CDN) to web application propertiesserviced by the CDN, the computer-executable instructions includinginstructions that are executable to cause the computer system to performat least the following: proactively allocate server resources of theedge data center among a plurality of web application properties, basedon one or more rules that are developed from first data collected aboutthe plurality of web application properties over a first time frame, andthat identify historic network demand for each of the plurality of webapplication properties, including: using the one or more rules todetermine a load type category for each web application property;computing at least one compatibility index for each of a plurality ofservers at the edge data center, including applying a penaltycoefficient to at least one server hosting a property type incompatiblewith at least one of the plurality of web application properties;creating at least one sorted order of the plurality of servers based onthe at least one compatibility index computed for each of the pluralityof servers; using the load type category of each web applicationproperty and the at least one sorted order of the plurality of serversto allocate a corresponding amount of server resources at the edge datacenter to each web application property, the server resources allocatedto each web application property used to cache data of the webapplication property and to serve the cached data to network clients,wherein the one or more rules cause web application properties havinghigher historic network demand to be allocated a greater amount ofserver resources at the edge data center than web application propertieshaving lower historic network demand; and sending data of each webapplication property over a network to the edge data center, for cachingthe data of each web application property at its corresponding allocatedserver resources and serving the cached data to network clients from thecorresponding allocated server resources; and reactively re-allocate theserver resources of the edge data center among the plurality of webapplication properties based on second data that is collected over asecond time frame that is shorter than the first time frame, to identifycurrent activity at the edge data center, and based on identifyingcurrently trending traffic associated with one or more of the pluralityof web application properties at the edge data center from the seconddata, including: determining from the second data that client networkdemands for at least one of the plurality of web application propertiesis currently increasing, and that trending traffic has caused the atleast one web application property to experience a load type categoryincrease; based on the at least one web application property havingexperienced a load type category increase, calculating additional serverresources at the edge data center needed to meet the current clientnetwork demands for the at least one web application property based on anew load type category for the at least one web application property;and allocating the additional server resources at the edge data centerto the at least one property, for caching the data of the at least oneweb application property at the additional server resources and servingthe cached data to network clients from the additional server resources.7. The computer program product as recited in claim 6, thecomputer-executable instructions also including instructions that areexecutable to configure the computer system, when allocating theadditional server resources for the at least one web applicationproperty, to allocate server resources from a plurality of partitions atthe same edge data center.
 8. The computer program product as recited inclaim 6, the computer-executable instructions also includinginstructions that are executable to configure the computer system, whencreating the at least one sorted order of the plurality of servers isalso based on a weighted average of one or more resources aggregatesindexes the at least one compatibility index computed for each of theplurality of servers.
 9. The computer program product as recited inclaim 6, the computer-executable instructions also includinginstructions that are executable to configure the computer system to:handle server deficit.
 10. The computer program product as recited inclaim 9, the computer-executable instructions also includinginstructions that are executable to configure the computer system, whenhandling server deficit, to request resources from one or more otheredge data centers.
 11. A computer system, comprising: one or moreprocessors; system memory; one or more data stores; and one or morecomputer-readable storage media having stored thereoncomputer-executable instructions that are executable by the one or moreprocessors to cause the computer system to allocate server resources atan edge data center of a content delivery network (CDN) to webapplication properties serviced by the CDN, the computer-executableinstructions including instructions that are executable to cause thecomputer system to perform at least the following: proactively allocateserver resources of the edge data center among a plurality of webapplication properties, based on one or more rules that are developedfrom first data collected about the plurality of web applicationproperties over a first time frame, and that identify historic networkdemand for each of the plurality of web application properties,including: using the one or more rules to determine a load type categoryfor each web application property; determining at least onecompatibility index for each of a plurality of servers at the edge datacenter, including applying a penalty coefficient to at least one serverhosting a property type incompatible with at least one of the pluralityof web application properties; creating at least one sorted order of theplurality of servers based on the at least one compatibility indexdetermined for each of the plurality of servers; using the load typecategory of each web application property and the at least one sortedorder of the plurality of servers to allocate a corresponding amount ofserver resources at the edge data center to each web applicationproperty, the server resources allocated to each web applicationproperty used to cache data of the web application property and to servethe cached data to network clients, wherein the one or more rules causeweb application properties having higher historic network demand to beallocated a greater amount of server resources at the edge data centerthan web application properties having lower historic network demand;and sending data of each web application property over a network to theedge data center, for caching the data of each web application propertyat its corresponding allocated server resources and serving the cacheddata to network clients from the corresponding allocated serverresources; and reactively re-allocate the server resources of the edgedata center among the plurality of web application properties based onsecond data stored in the one or more data stores that is collected overa second time frame that is shorter than the first time frame, toidentify current activity at an edge data center, and based onidentifying currently trending traffic associated with one or more ofthe plurality of web application properties at the edge data center fromthe first data, including: determining from the second data that clientnetwork demands for at least one of the plurality of web applicationproperties is currently increasing, and that trending traffic has causedthe at least one web application property to experience a load typecategory increase; based on the at least one web application propertyhaving experienced a load type category increase, calculating additionalserver resources at the edge data center needed to meet the currentclient network demands for the at least one web application propertybased on a new load type category for the at least one web applicationproperty; and allocating the additional server resources at the edgedata center to the at least one property, for caching the data of the atleast one web application property at the additional server resourcesand serving the cached data to network clients from the additionalserver resources.
 12. The computer system as recited in claim 11,wherein the second time frame is measured in one or more of minutes orseconds, and wherein the first time frame is measured in one or more ofdays, weeks, months, or years.
 13. The computer system as recited inclaim 11, the computer-executable instructions also includinginstructions that are executable to configure the computer system, whenallocating the additional server resources for the at least one webapplication property, to allocate server resources from a plurality ofpartitions at the same edge data center.
 14. The computer system asrecited in claim 11, the computer-executable instructions also includinginstructions that are executable to configure the computer system, whencreating the at least one sorted order of the plurality of servers isalso based on a weighted average of one or more resources aggregatesindexes the at least one compatibility index determined for each of theplurality of servers.
 15. The computer system as recited in claim 11,the computer-executable instructions also including instructions thatare executable to configure the computer system to: handle serverdeficit.
 16. The computer system as recited in claim 15, thecomputer-executable instructions also including instructions that areexecutable to configure the computer system, when handling serverdeficit, to request resources from one or more other edge data centers.17. The method as recited in claim 1, wherein the second time frame ismeasured in one or more of minutes or seconds, and wherein the firsttime frame is measured in one or more of days, weeks, months, or years.